code: def test_it_works():
      clf = ParallelPostFit(GradientBoostingClassifier())
  
      X, y = make_classification(n_samples=1000, chunks=100)
      X_, y_ = dask.compute(X, y)
      clf.fit(X_, y_)
  
      assert isinstance(clf.predict(X), da.Array)
      assert isinstance(clf.predict_proba(X), da.Array)
  
      result = clf.score(X, y)
      expected = clf.estimator.score(X_, y_)
      assert result == expected
  
imports: import dask
  import dask.array
  import dask.dataframe
  import numpy
  import pandas
  import pytest
  import sklearn.datasets
  from scipy.sparse import csr_matrix
  from sklearn.decomposition import PCA
  from sklearn.ensemble import GradientBoostingClassifier
  from sklearn.linear_model import LinearRegression, LogisticRegression, SGDClassifier
  from sklearn.naive_bayes import CategoricalNB
  from sklearn.preprocessing import OneHotEncoder
  from dask_ml.datasets import make_classification
  from dask_ml.utils import assert_eq_ar, assert_estimator_equal
  from dask_ml.wrappers import ParallelPostFit
label_desc: Use image node_id 1 for calling a global function with example usage: test_it_works() without return types